US8380558B1 - Method and system for analyzing shopping behavior in a store by associating RFID data with video-based behavior and segmentation data - Google Patents
Method and system for analyzing shopping behavior in a store by associating RFID data with video-based behavior and segmentation data Download PDFInfo
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- US8380558B1 US8380558B1 US11/999,717 US99971707A US8380558B1 US 8380558 B1 US8380558 B1 US 8380558B1 US 99971707 A US99971707 A US 99971707A US 8380558 B1 US8380558 B1 US 8380558B1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
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Abstract
Description
-
- Understanding aisle, section or shelf level behaviors in the context of the overall shopping trip
- Understanding differences in aisle, section or shelf level behaviors across various shopper segments such as:
- Shopper segments based on certain shopping trip characteristics
- Shopper segments based on purchase habits or “basket” characteristics
- Shopper segments based on existing loyalty card classifications
-
- Understanding aisle, section or shelf level behaviors in the context of the overall shopping trip
- Understanding differences in aisle, section or shelf level behaviors across various shopper segments such as:
- Shopper segments based on certain shopping trip characteristics
- Shopper segments based on purchase habits or “basket” characteristics
- Shopper segments based on existing loyalty card classifications
-
- to integrate the fragmented tracking segments by the plurality of means for capturing images that may not cover the retail space entirely but partially here and there, and
- to understand the customer's shopping behavior in the entire shopping path from one location to another location with the help of the RFID based proximity detection, even though the video-based tracking of the customer does not cover entire shopping path of the customer in the retail space.
TABLE 1 |
RFID TAG #37 |
12:00:01 PM | 12:00:10 PM | 12:00:11 PM | 12:10:11 PM | 12:10:15 |
READER | ||||
1 | |
|
|
|
TABLE 2 |
RFID TAG #37 |
12:00:01 PM | 12:00:10 PM | 12:00:11 PM | 12:10:11 PM | 12:10:15 PM |
(10, 10) | (5, 5) | (10, 10) | (10, 15) | (10, 20) |
-
- 1) Cart outside of camera view, Person inside of camera view
- 2) Cart outside of camera view, Person outside of camera view
- In this scenario, tracking is performed entirely by following the RFID signature as it travels past the various tag readers. No association with visual tracking data is possible.
- 3) Cart inside of camera view, Person outside of camera view
- 4) Cart inside of camera view, Person inside of camera view
-
- 1) Remove duplicate events indicative of when the cart is sitting still. For example, the following sequence:
- (POS1,t),(POS1,t+1),(POS1,t+2),(POS2,t+3),(POS2,t+4),(POS2,t+5) can be reduced to: (POS1,t),(POS1,t+2),(POS2,t+3),(POS2,t+5). The reduction is performed by checking to see if the position changed more than a predefined threshold, “thresh”.
- 2) Examine the (xr,yr,t) information for each of the reduced location sequences. If the visual track also exists at time t, then extract the position information at that time (xv,yv) from the visual track.
- 3) The RFID information immediately before and after the start and end of the visual track is always considered for comparison purposes.
TABLE 3 | |||
RFID | Closest Visual | ||
location/time | location/time | Non-Euclidean | Minimum required |
(xr, yr, t) | (xv, yv, t) | Distance | speed |
(0, 0, 0) | (5, 5, 10) | 20 | 2 |
(5, 5, 5) | (5, 4, 5) | 1 | N/a |
(10, 10, 10) | (9, 10, 10) | 1 | N/a |
(40, 40, 100) | (20, 20, 80) | 60 | 3 |
1−proximityError (1.1)
-
- Shopper segments based on certain shopping trip characteristics
- Shopper segments based on purchase habits or “basket” characteristics, and
- Shopper segments based on existing loyalty card classifications.
-
- 1) Maps: Display of quantitative measurement for overall shopping behavior,
- 2) Quantitative Measurement per Section, such as a ratio between shopping interaction levels,
level 2 overlevel 1, in a section, - 3) Dominant Path Measurement, which implies specific decision pattern because a finite number of next regions to choose from a “location A” defines the number of direction from that specific location and shows the tendency/preference of customers' decision for the next path,
- 4) Section Correlation of shopping paths for optimal distance between sections, and
- 5) Section Sequence, which includes the order of engagement.
Claims (14)
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US87641106P | 2006-12-21 | 2006-12-21 | |
US11/999,717 US8380558B1 (en) | 2006-12-21 | 2007-12-06 | Method and system for analyzing shopping behavior in a store by associating RFID data with video-based behavior and segmentation data |
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US9311799B2 (en) | 2014-03-18 | 2016-04-12 | Symbol Technologies, Llc | Modifying RFID system operation using movement detection |
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